Publication number  US7898472 B2 
Publication type  Grant 
Application number  US 12/412,672 
Publication date  Mar 1, 2011 
Filing date  Mar 27, 2009 
Priority date  Mar 27, 2009 
Fee status  Paid 
Also published as  CN101846735A, CN101846735B, DE102010012406A1, US20100245171 
Publication number  12412672, 412672, US 7898472 B2, US 7898472B2, USB27898472, US7898472 B2, US7898472B2 
Inventors  Shuqing Zeng 
Original Assignee  GM Global Technology Operations LLC 
Export Citation  BiBTeX, EndNote, RefMan 
Patent Citations (5), Referenced by (3), Classifications (10), Legal Events (9)  
External Links: USPTO, USPTO Assignment, Espacenet  
1. Field of the Invention
This invention relates generally to a system and method for determining the position and velocity of a plurality of vehicles relative to a host vehicle and, more particularly, to a system and method for determining the position and velocity of a plurality of vehicles relative to a host vehicle using GPS signals, where the system calculates optimal baselines between the vehicles.
2. Discussion of the Related Art
Shortbaseline precise relative positioning of multiple vehicles has numerous civilian applications. By using relative GPS signals in realtime, a vehicle can establish a subdecimeter level accuracy of relative positions and velocities of surrounding vehicles (vehicletovehicle object map) that are equipped with a GPS receiver and a data communication channel, such as a dedicated short range communications (DSRC) channel. This cooperative safety system can provide position and velocity information in the same way as a radar system.
For precise relative positioning, a vehicle needs to broadcast its raw GPS data, such as code range, carrier phase and Doppler measurements. The bandwidth required to do this will be an issue in a crowded traffic scenario where a large number of vehicles are involved.
Data format defined in The Radio Technical Commission for Maritime Service Special Committee 104 (RTCM SC104) contains unwanted redundancy. For example, message type #1 (L1C/A code phase correction) uniformly quantizes corrections with a 0.02 meter resolution. The pseudorange measurements are thus represented in a range of ±0.2×2^{15 }meters. However, the pseudorange measurements are generally limited to about ±15 meters. It is thus noted that excess bandwidth wastage occurs if the RTCM protocol is directly used in a cooperative safety system.
In accordance with the teachings of the present invention, a system and method are disclosed for determining the position and velocity of a plurality of vehicles relative to a host vehicle using GPS information. The method includes building a graph of the vehicles that define weighted baselines between each of the vehicles and the host vehicle and each of the vehicles where the weighted baselines define a geometric dilution of precision between the vehicles. The method then determines the optimal baseline having the lowest geometric dilution of precision between the host vehicle and each of the other vehicles using the weighted baselines based on the geometric dilution of precision. The method then computes the relative position and velocity between all of the vehicles and the host vehicle using the optimal baselines.
Additional features of the present invention will become apparent from the following description and appended claims, taken in conjunction with the accompanying drawings.
The following discussion of the embodiments of the invention directed to a system and method for determining the position and velocity of a plurality of vehicles relative to a host vehicle using GPS signals and optimalspanning tree analysis is merely exemplary in nature, and is in no way intended to limit the invention or it's applications or uses.
The data processing unit 24 can be further described as follows. Let X_{1},X_{2}, . . . , X_{K }be K vehicles. Let X_{i }be the state of the ith vehicle, including the position and velocity in Earthcentered and Earthfixed coordinates (ECEF). Let X_{H }be the state of the host vehicle 12, where 1≦H≦K. Let X be the states of the satellite, including the position and velocity in ECEF coordinates, which can be determined by the ephemeris messages broadcast by the jth satellite.
Double differential carrier phase measurements for short baselines are used to achieve a high positioning accuracy. Carrier phase measurements are preferred to code measurements because they can be measured to better than 0.01λ, where λ is the wavelength of the carrier signal, which corresponds to millimeter precision and are less affected by multipath than their code counterparts. However, carrier phase is ambiguous by an integer of the number of cycles that has to be determined during the vehicle operation.
Let the host vehicle X_{h }be the moving reference station. Let b_{ih }be a baseline between the host vehicle X_{h }and the remote vehicle X_{i}. The following doubledifference measurements of carrier phase, code and Doppler measurements can be written as:
d=H(X _{H} ,b _{ih})b _{ih} +λN+ν _{ih} (1)
Where H(X_{H},b_{ih}) is the measurement matrix depending on the moving host vehicle X_{H }and the baseline b_{ih}, λ is the wavelength of the carrier, N is the vector of the doubledifference of ambiguities and ν_{ih }is the unmodelled measurement noise. Without loss of generality, it is assumed that equation (1) is normalized, i.e., the covariance matrix of ν_{ih }is an identity matrix.
The heart of the flow chart diagram 60 is the onthefly (OTF) joint positioning and ambiguity determination module 62. In the module 62, a (6+J−1)dimension state tracking filter is employed to estimate the three position and the three velocity components, as well as J−1 float doubledifference of ambiguities as:
d={tilde over (H)}(X _{H} ,b _{ih})s+ν _{ih} (2)
Where {tilde over (H)}(X_{H},b_{ih}) is the extension of H(X_{H},b_{ih}) and the joint state
Note that the matrix {tilde over (H)}(X_{H},b_{ih}) is not very sensitive to changes in the host vehicle X_{h }and the baseline b_{ih}. With the process equation of the baseline available, it is usually sufficient to use the host vehicle X_{H }and the predicted estimate {tilde over (b)}_{ih }of the previous time instant. Therefore, when the value d is available, a better estimate of the baseline b_{ih }can be obtained by the filtering described below.
Let the process equation of the baseline b_{ih }be:
b _{ih}(t+1)=ƒ(b _{ih}(t))+w (3)
Where w denotes unmodeled noise.
In equation (3), ƒ is the function that expresses the dynamical model of the baseline. Some candidates of the dynamical model are a constant velocity model (CV) or a constant turning model (CT). Linearizing equation (3) in the neighborhood of the prediction of the baseline {tilde over (b)}_{ih }of the previous cycle and including the doubledifference of ambiguities N gives:
Where I is an identity matrix
and u=ƒ({tilde over (b)}_{ih})−F{tilde over (b)}_{ih}.
Now the OTF joint filtering procedure can be written in Algorithm 1, described below.
In equation (1), the measurement matrix H(X_{H},b_{ih}) plays an important role for the above single baseline positioning method to converge to the correct solution. A geometric dilution of precision (GDOP), i.e., [H(X_{H},b_{ih})]^{−1}, affects the quality of the estimate of the baseline b_{ih}. It can be verified that the GDOP depends on the number of shared satellites and the constellation of the common satellites for the baseline b_{ih}. For example, when visible shared common satellites between the remote vehicle and the host vehicle are close together in the sky, the geometry is weak and the GDOP value is high. When the visible shared common satellites between the remote vehicle and the host vehicle are far apart, the geometry is strong and the GDOP value is low. Thus, a low GDOP value represents a better baseline accuracy due to the wider angular separation between the satellites. An extreme case is that the GDOP is infinitely large when the number of shared satellites is less than four.
It is noted that there is more than one solution for positioning among multiple vehicles. Consider the scenario shown in
b _{AC} =b _{AB} +b _{BC} (5)
Similarly the baseline b_{AB }has two solutions. It can be verified that the qualities of the two solutions are different. The goal is to find the best solution. As shown in
The concept from
The step of computing the baselines to obtain the minimum GDOP in the flow diagram 110 can be performed any algorithm suitable for the purposes described herein. A first algorithm, referred to as Algorithm 1, is based on a single baseline precision positioning. Given the previous estimate of the joint state ŝ(t−1) with its covariance matrix {circumflex over (P)}(t−1); double difference d; GPS time stamp of the receiver t_{R}; satellite ephemeris E; the system dynamical of equation (1); the measurement equation (2); the covariance matrix Q of the noise term w in equation (3); and the covariance matrix R of the noise term ν in equation (2).
The updated estimate of the joint state ŝ(t) and the covariance matrix {circumflex over (P)}(t) at time t can be solved as follows:
1. Compute the prediction {tilde over (s)} using equation (1) as:
2. Compute the innovation error as:
e=d−{tilde over (H)}({tilde over (s)})
With {tilde over (H)}={tilde over (H)}(X_{h},b_{ih})
3. Compute innovation covariance S={tilde over (H)}{tilde over (P)}{tilde over (H)}^{T}+R.
4. Compute Kalman gain as K={tilde over (P)}{tilde over (H)}^{T}S^{−1}.
5. Output the updated estimate ŝ={tilde over (s)}+Ke and the covariance matrix {circumflex over (P)}=(I−K{tilde over (H)}){tilde over (P)}.
The precise relative positioning for multiple vehicles can also be determined by the following algorithm, referred to as Algorithm 2.
1. Build a weighted graph G of vehicles where each vehicle is a vertex and adding an edge between two vertices if the number of shared observed satellites is larger or equal to four. Let the root denote the host vehicle.
2. The weight of an edge is equal to the geometric dilution of precision (GDOP) of the common satellites observed by the two vehicles, i.e., det [H(X_{i},b_{ik})]^{−1 }in equation (1) for the weight of the edge between the vertices i and j.
3. Use dynamic programming (either revised BellmanFord algorithm of Algorithm 3 or Dijkstra algorithm of Algorithm 4) to find a spanning tree such that the path from any other nodes has the best satellite geometry (minimum GDOP) for positioning.
4. For all E in the graph G do
5. Determine the baseline as represented by the edge E by the algorithms described in Algorithm 1.
6. end for
7. Compute relative positions and velocities from the vehicles to the host vehicle based on the graph G.
Algorithm 3 is a Reversed BellmanFord Algorithm
Given the graph G with the vertices V={ν_{i}1≦i≦V}, the edges E={e_{k}1≦k≦E} and the weights of edges {w_{k}1≦k≦E}; and the source of vertex H.
Ensure: The span tree T and C:
1.  for all vertex v in the set of vertices do 
2.  if v is the source then 
3.  Let the cost (v) be 0. 
4.  else 
5.  let the cost(v) be ∞. 
6.  end if 
7.  Let predecessor(v) be null. 
8.  end for 
9.  for i from 1 to V − 1 do 
10.  for each edge e_{k }in E do 
11.  Let u be the source vertex of e. Let v be the destination 
vertex of e_{k}.  
12.  if cost(v) is less than max(w_{k}, cost(u)), then 
13.  Let cost(v)=max(w_{k}, cost(u)). 
14.  Let predecessor(v) = u. 
15.  end if 
16.  end for 
17.  end for 
18.  Construct the span tree T using the predecessor(v) for all 
vertices.  
Algorithm 4 is a Revised Dijkstra Algorithm:
Given the graph G with the vertices V={ν_{i}1≦i≦V}, the edges E={e_{k}1≦k≦E} and the weights of edges {w_{k}1≦k≦E}; and the source of vertex H.
Ensure: The span tree T and G:
1.  for all vertex v in the set of vertices do 
2.  if v is the source H then 
3.  Let the cost (v) be 0. 
4.  else 
5.  let the cost(v) be ∞. 
6.  end if 
7.  Let predecessor(v) be null. 
8.  end for 
9.  Let the set Q contain all vertices in V. 
10.  for Q is not empty do 
11.  Let u be vertex in Q with smallest cost. Remove u from 
Q.  
12.  if for each neighbor v of u do 
13.  Let e be the edge between u and v. Let alt = 
max(cost(u), weight(e)).  
14.  if alt<cost(v) then 
15.  cost(v)=alt 
16.  Let predecessor(v) be u. 
17.  end if 
18.  end for 
19.  end for 
20.  Construct the span tree T using the predecessor(v) for all 
vertices.  
GPS measurements are correlated through the latent vector of the position and velocity of a GPS receiver, which can be expressed as follows.
Let X be a sixdimension latent state vector consisting of the positions and velocities in ECEF coordinates. Let C be a satellite constellation including the positions and velocities of the satellites in ECEF coordinates, which can be determined by the ephemeris messages broadcasted by the satellites. Let a GPS measured quantity O include the code range, carrier phase and Doppler shift for the receiver from the satellites. As a result, the measurement equation can be written as;
O=h(X,β,{dot over (β)},C)+ν (6)
Where β is the host receiver clock error, {dot over (β)} is the change rate of β and ν is the unmodeled noise for GPS measurements including biases caused by ionospheric and tropospheric refractions, satellite orbital errors, satellite clock drift, multipath, etc.
The standalone absolute positioning module 132 monitors the input of measurements including code range, carrier phase and Doppler shift, input of satellite constellation C, and vehicle data (e.g., wheel speed and yaw rate). The module 132 generates the absolute position and velocity of the GPS receiver {circumflex over (X)}. The module 132 also generates the predicted GPS measurements Õ as expressed by a function h as:
Õ=h({circumflex over (X)},C) (7)
Therefore, the innovation error e can be defined as:
e=O−Õ (8)
It can be verified that the innovation error vector has two properties. The components are mutually uncorrelated and for each component, the variance is much less than the counterpart of the GPS measurements O. Thus, standard data compression methods, such as, but not limited to, vector quantization or Huffman coding, can be applied to the innovation error e and achieves good compression performance.
Let the estimate of the absolute position and velocity of the GPS receiver be {circumflex over (X)}. The compressed innovation errors is decoded to obtain the corresponding innovation error e. One can verify that the predicted measurements Õ can be computed from the estimate of the absolute position and velocity of the GPS receiver {circumflex over (X)} and the satellite constellation C as:
Õ=h({circumflex over (X)},C) (9)
Thus, the recovered GPS measurements can be computed as:
Õ=Õ+e (10)
The GPS measurements are highly correlated with time. This makes them well suited to compression using a prediction model of the latent state vector X. Let the process equation of the latent state at time instant t be:
X(t+1)=ƒ(X(t))+w (11)
Where ƒ is the system process function of the host vehicle (e.g., constant velocity model, or constant turning model) where the GPS receiver is mounted on the roof of the vehicle and w is the unmodeled noise in the process equation.
The residuals w in equation (11) are well suited to compression by encoding the difference between the current state vector and the predicted state vector from the previous time instant.
The minimum description length compression of the GPS protocol (MDLCOG) is designed as an application layer provided above a transport layer, as shown in
The MDLCOG consists of a collection of messages, known as frames, used for initializing and transmission of measurements and additional data, such as GPS time stamp and a bitmap of the observed satellites. These data frames are known as an initialization frame (Iframe), an additional data frame (Aframe), a differential frame (Dframe) and a measurement frame (Mframe).
At the beginning of the data transmission, the encoder sends an Iframe to initialize the state prediction module at the decoder. The Iframe is analogous to the key frames used in video MPEG standards. The Iframe contains the absolute position and velocity of the GPS receiver in ECEF coordinates estimated by the encoder. The Iframe is also sent whenever the difference between the current and previous estimates of the latent state X is larger than a threshold.
The Aframe contains the nonmeasurement data, such as the satellite list, data quality indicators, etc. The Aframe is transmitted only at startup and when the content changes.
The most frequently transmitted frames are the Dframe and the Mframe. Dframes are analogous to the Ppicture frames used in MPEG video coding standards in the sense that they are coded with reference to previously coded samples. The time series difference in the Dframe use the vehicle dynamical model expressed in equation (11). Each Dframe contains the Huffman coded difference between the current and previous estimates of the latent state X. The Mframe contains the GPS time stamp and the Huffman coded difference between the measurements O and the predicted values Õ. The Mframe is sent whenever a new GPS measurement is received, and separate frames are transmitted for L1 and L2 frequencies for the Mframe.
Once the decoder has been initialized, having received the appropriate Iframes and Aframes, the encoder transmits the quantized prediction residual for each epoch in the corresponding Dframe and Mframe. An example of the sequence of frames is shown in
The algorithm then determines if the satellite map has changed at decision diamond 238, and if so, the algorithm generates an Aframe at box 240. Particularly, if the identities, i.e., PRN, of the satellite constellation C are changed from the previous time instant, an Aframe is generated to encode the list of the observed satellite's PRN. The frame consists of a 32bit map, where each bit is either true or false depending on the presence data for a particular satellite.
The algorithm then estimates the standalone position and velocity of the vehicle at box 242. In the estimating standalone position and velocity module, a Kalman filter is used to estimate the latent state vector X through a series of measurements O. Let the latent state vector X=(x,y,z,{dot over (x)},{dot over (y)},ż,β,{dot over (β)}) denote the concatenated vector of a threedimensional position vector in the ECEF coordinates, threedimensional velocity vector in the ECEF coordinates, receiver clock error, and change rate of the receiver clock error, respectively. The linearized system of equation (6) at the neighborhood of X* can be written as:
X(t+1)=FX(t)+u _{1} +w (12)
Where F is the Jacobian matrix with respect to the latent state vector X and the nonlinear term u_{1}=ƒ(X*)−FX*.
The measurements of equation (6) of the jth satellite can be expanded into:
For j=1, . . . , J, where ρ^{j }is the geometrical range between the receiver and the jth satellite {dot over (ρ)}^{j }is the projection of the velocity vector of the jth satellite projected onto the direction of from the receiver to the satellite, c denotes the speed of light, λ and f are the wavelength and frequency of the carrier signal, respectively, ν_{R},ν_{φ} and ν_{D }are the unmodeled measurement noise for pseudorange, carrier phase and Doppler shift, respectively, and x_{j},y_{j }and z_{j }are the threedimensional position of the jth satellite in the ECEF coordinates.
Note that the quantities ρ^{j }and {dot over (ρ)}^{j }depend on the vector of the latent state vector X. In other words, equation (13) includes nonlinear equations in terms of the latent state vector X. These quantities are not very sensitive to changes in the latent state vector X. With the receiver's dynamic available, it is usually sufficient to use the predicted estimate {tilde over (X)} of previous time instant as the center of the linearized neighborhood X* and use it to replace the latent state vector X in equation (13). Therefore, when R_{j}, Φ_{j }and D_{j }are available, a better estimate of the latent state vector X can be obtained by the filtering method described in Algorithm 5 detailed below.
Equation (13) can be linearized in the neighborhood of X* as:
O _{j} =H _{j} X+u _{2} _{ j }+ν_{j} (14)
Where O_{j}=[R_{j},Φ_{j},D_{j}]^{T},H_{j }is the Jacobian of equation (13) matrix with respect to the latent state vector X and the nonlinear term u_{2} _{ j }=h(X*)−H_{j}X*. Therefore, the key steps of the estimating standalone position and velocity module can be outlined in Algorithm 5.
The algorithm then determines whether the current state estimate X(t) and the previous state estimate X(t−1) is larger than a threshold T at decision diamond 244. If the difference between the current state estimate X(t) and the previous state estimate X(t−1) is larger than the threshold T, an Iframe is generated at box 248. The Iframe encodes the current state estimate X(t), including the ECEF position and velocity of the receiver. Otherwise a Dframe is generated to encode the difference X(t)−X(t−1) using Huffman codeword dictionary at box 246.
The next step is to calculate the model residuals at box 250 for the measurements as:
e=O−h(X) (15)
Then, the measurement model residuals are encoded using the Huffman codeword dictionary by generating the Mframe at box 252. In the last step at box 254, all generated frames are transmitted to the lower UDP layer 196.
Algorithm 5, Absolute Positioning Update:
Given the previous estimate of the latent state {circumflex over (X)}(t−1) with its covariance matrix {circumflex over (P)}(t−1); measurements O(t); GPS time stamp of the receiver t_{R}; satellite ephemerides E; the system dynamical equation (4); measurement equation (6); covariance matrix Q of the noise term w in equation (4); covariance matrix R of the noise term ν in equation (6).
The updated estimate of the absolute position and velocity of the receiver {circumflex over (X)}(t) at time t.
1. Compute the prediction {tilde over (X)} = f ({circumflex over (X)}(t − 1)) and {tilde over (P)} = F{tilde over (P)}(t − 1)F^{T} + Q. 
2. for all j, 1 ≦ j ≦ J do 
3. Retrieve the satellite ephemeris of the jth satellite. 
4. Compute the ECEF position X_{j} = [x_{j}, y_{j}, z_{j}]^{T} and velocity 
{dot over (X)}_{j} = [{dot over (x)}_{j}, {dot over (y)}_{j}, ż_{j}]^{T} of the jth satellite. 

{dot over (X)}_{j} ^{T} (X_{j}− {tilde over (X)}) with {tilde over (X)} = {tilde over (x)}, {tilde over (y)}, {tilde over (z)}]^{T }being the prediction of ECEF position 
of the receiver. 
6. Compute H_{j} using equation (7): 

7. end for 
8. Compute H = [H_{1} ^{T}, . . . , H_{J} ^{T}]^{T}. 
9. Compute innovation error using equation (5), i.e., e = O(t) − h({tilde over (X)}) 
10. Compute innovation covariance S = H{tilde over (P)}H^{T }+ R. 
11. Compute Kalman gain K = {tilde over (P)}H^{T}S^{−1}. 
12. Output the updated estimate {circumflex over (X)} = {tilde over (X)} + Ke and the covariance 
matrix {circumflex over (P)} = (I − KH){tilde over (P)}. 
The foregoing discussion discloses and describes merely exemplary embodiments of the present invention. One skilled in the art will readily recognize from such discussion and from the accompanying drawings and claims that various changes, modifications and variations can be made therein without departing from the spirit and scope of the invention as defined in the following claims.
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Citing Patent  Filing date  Publication date  Applicant  Title 

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U.S. Classification  342/357.34, 342/451, 342/458 
International Classification  G01S19/51 
Cooperative Classification  G01S5/0289, G01S5/0072, G01S19/51 
European Classification  G01S5/02R1, G01S19/51, G01S5/00R3 
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